Shortcuts

Source code for ignite.contrib.metrics.regression.fractional_absolute_error

from __future__ import division

import torch

from ignite.exceptions import NotComputableError
from ignite.contrib.metrics.regression._base import _BaseRegression


[docs]class FractionalAbsoluteError(_BaseRegression): r""" Calculates the Fractional Absolute Error. :math:`\text{FAE} = \frac{1}{n}\sum_{j=1}^n\frac{2 |A_j - P_j|}{|A_j| + |P_j|}` where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - `update` must receive output of the form `(y_pred, y)`. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 """ def reset(self): self._sum_of_errors = 0.0 self._num_examples = 0 def _update(self, output): y_pred, y = output errors = 2 * torch.abs(y.view_as(y_pred) - y_pred) / (torch.abs(y_pred) + torch.abs(y.view_as(y_pred))) self._sum_of_errors += torch.sum(errors).item() self._num_examples += y.shape[0] def compute(self): if self._num_examples == 0: raise NotComputableError('FractionalAbsoluteError must have at least ' 'one example before it can be computed.') return self._sum_of_errors / self._num_examples

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 05/04/2022, 8:31:22 PM.

Built with Sphinx using a theme provided by Read the Docs.